In this modern era of technology, most of the organization adopted to computational system for almost all the activities. Similarly the education system has also adopted computational-based decision support system to carry out all the academic activities since the student join the organization until he completes his degree. The existing decision support systems operational in Pondicherry University was studied and several issues were found such as there is a lack of real-world validation, domain specific subject selection, non-adaptable to the new regulations time to time issued by UGC, for offline and online two different modules are used which leads to various data anomalies. System automation for an Admission and Student Information System revolutionizes the way educational institutions manages admission and student data, by leveraging technology. OASIS automation system address to streamlines the online application submissions, exam registration, and application tracking. It also enhances student information management by mechanizing tasks such as record creation, course registration, and fee payment processing. Additionally, this system will enable data analytics for decision-making and seamless integration with other systems to ensure compliance, enhanced security, and improve performance. This paper proposes to implement a generic framework using supervised learning models like (DT, LR, RF, etc.) for decision support system that aims to address the challenges faced by university-affiliated colleges existing examination management systems while managing interdisciplinary higher education courses and reduce dropout rates while ensuring compliance with evolving regulations and also identifies the subject’s that needs to be modified to improve the academic performance.